Metacognitive Self Mod

# Execution Trace Capture

Record execution traces so that metacognitive-self-mod
can analyze concrete decision sequences, not just
aggregate metrics. Inspired by Microsoft Trace
(AutoDiff-like backward propagation through execution
traces), Trajectory-Informed Memory Generation (arXiv
2603.10600, decision attribution from trajectories),
and the ACE framework (evolving playbooks via
generation, reflection, and curation).

## Trace Structure

Each trace captures a single skill invocation from start
to finish. The trace body sits inside the shared
session-capture envelope (ADR-0011) so friction signals
and traces can be consumed through one parser:

```json
{
  "schema_version": "session-capture/1",
  "session_id": "2026-04-14-abc12345",
  "timestamp": "2026-04-14T10:30:00Z",
  "source": "trace-capture",
  "payload": {
    "trace_id": "session-{date}-{hash}",
    "skill": "attune:project-execution",
    "started": "2026-04-14T10:30:00Z",
    "completed": "2026-04-14T10:32:15Z",
    "outcome": "success",
    "capture_mode": "decision-only",
    "steps": [
      {
        "tool": "Read",
        "target": "src/main.py",
        "purpose": "understand entry point",
        "result": "success",
        "tokens_used": 1200,
        "decision_point": false
      },
      {
        "tool": "Edit",
        "target": "src/main.py:45",
        "purpose": "add error handling",
        "result": "success",
        "tokens_used": 800,
        "decision_point": true,
        "alternatives_considered": [
          "try/except",
          "result type",
          "assertion"
        ],
        "rationale": "try/except matches existing patterns"
      }
    ],
    "attribution": {
      "success_factors": [
        "followed existing patterns",
        "tested incrementally"
      ],
      "failure_factors": [],
      "key_decisions": [
        "chose try/except over result type at step 4"
      ]
    }
  }
}
```

Legacy traces written before envelope adoption are read
as ``session-capture/0`` (entire file treated as the
payload). See ``docs/adr/0011-session-capture-envelope.md``
for the contract and migration path.

## Capture Modes

Not every invocation needs a full trace. Three modes
control the recording fidelity.

| Mode | Records | When to use |
|------|---------|-------------|
| `minimal` | Outcome and duration only | High-trust T3 skills |
| `decision-only` | Decision points, outcome, duration | Default for all skills |
| `full` | Every tool call, token counts, all fields | Skills with <85% success rate |

**Mode selection logic:**

- Default: `decision-only` (captures rationale without
  flooding storage).
- Enable `full` with `--trace=full` or automatically for
  any skill whose rolling success rate falls below 85%.
- Use `minimal` for T3 skills that consistently succeed
  and need only aggregate trend data.

## What to Capture

- **Tool calls** (full mode only): tool name, target,
  purpose, result, approximate tokens consumed.
- **Decision points** (all modes except minimal):
  alternatives considered (2-5), rationale for the
  chosen option, whether later revised.
- **Trace completion** (all modes): overall outcome
  (`success`/`failure`/`partial`), wall-clock duration,
  total tokens consumed.

## Attribution Analysis

After a trace completes, run backward attribution to
identify which decisions drove the outcome. This follows
the Microsoft Trace principle of propagating feedback
backward through the execution path.

**For successful traces:**

1. Identify decisions that aligned with known success
   patterns (from `improvement_memory.json`).
2. Flag novel successfu